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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import numpy as np
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
class WrapperBase:
def close(self):
pass
class SP_ONNXClassifierWrapper(WrapperBase):
def __init__(
self,
file,
target_dtype=np.float32,
batch_size=1,
max_keypoints=512,
descriptor_dim=256,
):
self.target_dtype = target_dtype
self.load(file)
self.stream = None
self.max_keypoints = max_keypoints
self.batch_size = batch_size
self.descriptor_dim = descriptor_dim
def load(self, file):
f = open(file, "rb")
self.runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING))
self.engine = self.runtime.deserialize_cuda_engine(f.read())
self.context = self.engine.create_execution_context()
def allocate_memory(self, batch):
self.keypoints = np.empty(
(self.batch_size, self.max_keypoints, 2), dtype=np.float32
)
self.scores = np.empty((self.batch_size, self.max_keypoints), dtype=np.float32)
self.descriptors = np.empty(
(self.batch_size, self.max_keypoints, self.descriptor_dim), dtype=np.float32
)
# Allocate device memory
self.d_input = cuda.mem_alloc(1 * batch.nbytes)
self.d_keypoints = cuda.mem_alloc(1 * self.keypoints.nbytes)
self.d_scores = cuda.mem_alloc(1 * self.scores.nbytes)
self.d_descriptors = cuda.mem_alloc(1 * self.descriptors.nbytes)
tensor_names = [
self.engine.get_tensor_name(i) for i in range(self.engine.num_io_tensors)
]
self.context.set_tensor_address(tensor_names[0], int(self.d_input))
self.context.set_tensor_address(tensor_names[1], int(self.d_keypoints))
self.context.set_tensor_address(tensor_names[2], int(self.d_scores))
self.context.set_tensor_address(tensor_names[3], int(self.d_descriptors))
self.stream = cuda.Stream()
def predict(self, batch):
if self.stream is None:
self.allocate_memory(batch)
# Transfer input data to device
cuda.memcpy_htod_async(self.d_input, batch, self.stream)
# Execute model
self.context.execute_async_v3(self.stream.handle)
# Transfer predictions back
cuda.memcpy_dtoh_async(self.keypoints, self.d_keypoints, self.stream)
cuda.memcpy_dtoh_async(self.scores, self.d_scores, self.stream)
cuda.memcpy_dtoh_async(self.descriptors, self.d_descriptors, self.stream)
# Syncronize threads
self.stream.synchronize()
return self.keypoints, self.scores, self.descriptors
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